"""simple docstring"""



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:List[str]									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int									=			{"""vocab_file""": """spm_char.model"""}

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

SCREAMING_SNAKE_CASE__:Dict									=			{
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[str]							  =						VOCAB_FILES_NAMES
 _snake_case       :					List[str]							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					int							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case       :					Any							  =						["""input_ids""", """attention_mask"""]


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase="<s>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCamelCase    ,		eos_token=lowerCamelCase    ,		unk_token=lowerCamelCase    ,		pad_token=lowerCamelCase    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**lowerCamelCase    ,		)

        __a       =    vocab_file

        __a       =    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(lowerCamelCase						)


 @property
 def 						a__   ( self						):
        return self.sp_model.get_piece_size()


 def 						a__   ( self						):
        __a       =    {self.convert_ids_to_tokens(lowerCamelCase						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab


 def __getstate__( self						):
        __a       =    self.__dict__.copy()
        __a       =    None
        return state


 def __setstate__( self    ,		lowerCamelCase						):
        __a       =    d

        # for backward compatibility
        if not hasattr(self    ,		"sp_model_kwargs"						):
               __a       =    {}

        __a       =    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(self.vocab_file						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.encode(lowerCamelCase    ,		out_type=lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.sp_model.piece_to_id(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.sp_model.IdToPiece(lowerCamelCase						)
        return token


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    []
        __a       =    ""
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(lowerCamelCase						) + token
                      __a       =    []
               else:
                      current_sub_tokens.append(lowerCamelCase						)
        out_string += self.sp_model.decode(lowerCamelCase						)
        return out_string.strip()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = False						):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase    ,		token_ids_a=lowerCamelCase    ,		already_has_special_tokens=lowerCamelCase						)

        __a       =    [1]
        if token_ids_a is None:
               return ([0] * len(lowerCamelCase						)) + suffix_ones
        return ([0] * len(lowerCamelCase						)) + ([0] * len(lowerCamelCase						)) + suffix_ones






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        if not os.path.isdir(lowerCamelCase						):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
               return
        __a       =    os.path.join(
            lowerCamelCase    ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase						) and os.path.isfile(self.vocab_file						):
               copyfile(self.vocab_file    ,		lowerCamelCase						)
        elif not os.path.isfile(self.vocab_file						):
               with open(lowerCamelCase    ,		"wb"						) as fi:
                      __a       =    self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase						)

        return (out_vocab_file,)

"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
